Method and System for Complex Smart Grid Infrastructure Assessment

ABSTRACT

An infrastructure assessment system integrates with a smart grid infrastructure at all layers of the infrastructure. Data may be collected across layers. Performance metrics may be monitored and simulations may be performed. Action items may be decided upon based on actual behavior of the infrastructure determined from the collected data and on predicted behavior from simulations of the infrastructure. The action items may then be dispatched to be performed on the infrastructure. The effect of the management actions can then be “acquired” by the system via detailed monitoring and can be used, for example, to measure the effectiveness of the decisions or recalibration of the whole system.

BACKGROUND

Unless otherwise indicated herein, the approaches described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

The term “smart grid” is often used to refer technology that utilitycompanies use to monitor and control the delivery andproduction/consumption of a resource such as electricity, gas, water,etc., using computer-based remote control and automation. The smart gridis empowered by information technology (IT) tools for monitoring andcontrol. The smart grid envisions several sophisticated services, whichheavily depend on near real-time monitoring of the assets andfunctionality they provide. However, we still face several problems whenit comes down to assessment of the infrastructure, not to mentionestimation of behavior. Today it is very difficult to: (i) anticipatethe requirements for all of its layers e.g., of a smart meteringdeployment needed in hardware and software; and (ii) modify, on the fly,the infrastructure to guarantee envisioned constraints such asperformance or quality of service (QoS). The smart grid promises a moreversatile and intelligent network of collaborating actors that willeventually lead to better utilization of its resources in order toachieve goals such as energy efficiency. The smart grid is acyber-physical system (CPS) that depends on IT and has spawned severaltraditional domains and (business) processes (e.g. industrialautomation, smart metering, etc.) in an effort to deliver an optimizedcritical energy infrastructure and auxiliary services.

As users in the smart grid era will be able to not only consume but alsoproduce energy (referred to as “prosumers”), the dynamics and complexityof the system increases. Information and communication technologies maybe employed to provide insight to the prosumer's current and futureactivities that is not possible in the conventional grid. In the future,devices may no longer be single role devices that either only consumeenergy (e.g., a home appliance) or only produce energy (e.g., aphotovoltaic panel), but rather will have interchangeable dual roles ofenergy consumer and energy producer, and hence the term “prosumerdevices.” A typical example of a prosumer device is the electric car,which consumes electricity when driven, and produces electricity that isstored when braking. A commonly described usage scenario involves afleet of electric cars. While the cars are being driven or charged, theycan be viewed as “consuming” energy. However, if the need arises, theycan feed the energy stored in their batteries to the grid as providers.

As energy monitoring and management systems become increasinglyintegrated with enterprise systems, enterprise services will integrateinformation coming from highly distributed smart metering points in nearreal-time, process it, and take appropriate decisions. The decisionmaking process can consider prosumer-specific behavioral informationeither measured, assumed, or explicitly provided by the prosumer. Thiswill give rise to a new generation of applications that depend on“real-world” services which constantly hold actualized data as they aregenerated. Furthermore, the integration of potential future behavior ofthe prosumer may enable better correlation and analytics. Suchinformation is usually not available at all, or in the best case onlyacquired by local systems (e.g., a building's energy management system),and over dedicated channels and proprietary interfaces that hinderfurther dissemination of the information.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a high level illustration of a smart grid architecture inaccordance with the present disclosure.

FIG. 2 is a system block diagram of an illustrative embodiment of aninfrastructure assessment system (for monitoring and management).

FIGS. 2A and 2B illustrate alternate embodiments, showing differentconfigurations of the cockpit module.

FIG. 3 is a process flow illustrating the role of an infrastructureassessment system in a pre-deployment scenario.

FIG. 4 is a process flow illustrating the roll of an infrastructureassessment system in an existing smart grid deployment.

FIG. 5 shows an example of a computer system configured according to thepresent disclosure.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousexamples and specific details are set forth in order to provide athorough understanding of the present disclosure. It will be evident,however, to one skilled in the art that the present disclosure asdefined by the claims may include some or all of the features in theseexamples alone or in combination with other features described below,and may further include modifications and equivalents of the featuresand concepts described herein.

FIG. 1 illustrates an infrastructure assessment system 100 in accordancewith embodiments of the present disclosure integrated in a smart gridinfrastructure 102. A typical configuration of the smart gridinfrastructure 102 is a three-layered hierarchical structure, similar towhat is depicted in FIG. 1.

A prosumer device layer 122 may be viewed as producers of data withinthe smart grid infrastructure 102. This layer hosts all classes ofprosumer devices 132, 134, 136, including strictly consuming devices andstrictly producing devices as well as devices that consume and produce.Prosumer device 132-136 may be complex devices or entire systems.Prosumer devices 132-136 may include sufficient computing capacity andcommunication capability in order to communicate data (whetherwirelessly or wired) to higher layers 124, 126 in the smart gridinfrastructure 102. Illustrative examples of strictly consuming devicesinclude household appliances. For example, a washing machine may be ableto report data to the smart grid infrastructure 102 about itselectricity consumption and water consumption. An electric meter may beable to report data to the smart grid infrastructure 102 aboutelectricity consumed by the household, and so on. An illustrativeexample of a prosumer device is the electric car, which consumeselectricity when accelerating and produces electricity when braking.Solar cells are an example of a strictly producing device.

Prosumer devices 132-136 may report data to the higher layers 124, 126in the smart grid infrastructure 102. The data may include resourceconsumption rates (e.g., electricity, water, etc.), resource productionrates (e.g., electricity), and so on. The data may be communicated to aninformation processing layer 124 or directly to an enterprise serviceslayer 126. The data may be encrypted or communicated over a private orotherwise secured communication channel such as illustrated by prosumerdevices 136. Household electric smart meters, for example, typicallysend their data in a secured way in order to preserve the privacy of thehousehold. A prosumer device 136 may be gateway, a mediator, or anyother physical or non-physical (virtual/proxy) device. Other prosumerdevices 132, 134 may send their data in plaintext over an opencommunication channel such as the Internet, or may employ someintermediate level of security.

The smart grid infrastructure 102 may include an information processinglayer 124 to which devices in the prosumer device layer 122 communicate.FIG. 1, for example, shows that devices 134, 136 communicate with theinformation processing layer 124. The information processing layer 124may serve as a gateway for data gathered from prosumer devices 134, 136,and may serve to handle proprietary protocols, to improve performance,to perform preprocessing, to accommodate infrastructure restrictions,and so on. The information processing layer 124 may include informationconcentrators 142, 144 to facilitate communications with the prosumerdevices 134, 136. Concentrators 142, 144 may aggregate, filter, orotherwise process data collected from the prosumer devices 134, 136. Forexample, some prosumer devices 136 may employ a proprietarycommunication protocol. Concentrator 144 may be configured to handle theproprietary protocol. The information concentrators 142, 144 mayaggregate the data or perform domain-specific analytics, and so on.Collected data may be pre-filtered before being passed on higher layersin the smart grid infrastructure 102. Other such data pre-processingfunctions may be performed by the information processing layer 124depending on the requirements of the higher layers in the smart gridinfrastructure 102 such as the enterprise services layer 126.

The enterprise services layer 126 may provide services to variousbusiness processes 128 a, 128 b, 128 c that depend on the real-worlddata supported by the smart grid infrastructure 102. Business processes128 a, 128 b, 128 c may be steps within a larger process, a set ofdistributed processes, and so on. A system in the enterprise serviceslayer 126 might be a metering data unification system, for example, toprovide services such as billing. The enterprise services layer 126 mayreceive data from the information processing layer 124. Data may bereceived directly from the prosumer device layer 122 as well. Theenterprise services layer 126 provides services to business processes128 a, 128 b, 128 c, which may be deemed consumers of data produced inthe smart grid infrastructure 102. Services provided by the enterpriseservice layer 126 may be distributed over a cloud infrastructure.

In accordance with principles of the present disclosure, aninfrastructure assessment system 100 may be integrated into the smartgrid infrastructure 102. The infrastructure assessment system 100 maycollect real-time data from the smart grid infrastructure 102. Theinfrastructure assessment system 100 may assess the general health ofthe smart grid infrastructure 102 and deploy maintenance and othermanagement services into the smart grid infrastructure based on itsassessment. In some embodiments, the integration of the infrastructureassessment system 100 into the smart grid infrastructure 102 may includeone or more communication channels 112, 114, 116. The infrastructureassessment system 100 may collect data from the enterprise serviceslayer 126 over communication channel 112. Data may also be collecteddirectly from the lower layers 124, 122. For example, data collectedfrom the information processing layer 124 (e.g., via communicationchannel 114) may provide a more granular view into the health of thesmart grid infrastructure 102. Data collected directly from the prosumerdevices 132-136 (e.g., over communication channel 116) may deemed asbeing the “raw” data that feeds into the smart grid infrastructure 102,and thus may provide the highest resolution view of the smart gridinfrastructure.

Referring to FIG. 2, an illustrative embodiment of an infrastructureassessment system 100 in accordance with the present disclosure maycomprise several components. It is noted that the components may beinterconnected in many ways and that the figure highlights only some ofthose connections.

The infrastructure assessment system 100 may include a receiver module202 which serves as an interface to the “data sources” in the smart gridinfrastructure 102 to collect data from the smart grid infrastructure.The collected data may include data collected from the enterpriseservices layer 122, the information processing layer 124, and individualprosumer devices 132-136 in the prosumer devices layer 126. In someembodiments, the receiver module 202 may comprise suitable communicationfacilities, both hardware and software, to enable communication with theenterprise services layer 126, the information processing layer 124, andthe prosumer devices 132-136 in the prosumer device layer 122. Forexample, communication with the enterprise layer 126 and the informationprocessing layer 124 may call for a suitable Internet connection.Communications may be secured, for example HTTPS may be used over a webconnection. Communication with prosumer devices 132-136 may requirespecialized hardware (e.g., radio communication equipment) and/orspecial software (e.g., private communication protocol, encryption,etc.), depending on the prosumer device. The data collected by thereceiver module 202 may be deemed real-time data because, for example,data from the prosumer devices 132-136 can be collected as it is beinggenerated.

The infrastructure assessment system 100 may include a cockpit module204, which may serve as a direct interface for users 104 of theinfrastructure assessment system. The cockpit module 204 may serves asan entry point to access the data and services provided by theinfrastructure assessment system 100. In some embodiments, the variousmodules in the infrastructure assessment system 100 (e.g., modules 212,222, 224, 232, 242, 244, and 256) may be configured to operate asseparate sub-systems or processes, and thus may be viewed as independentdata sources in the infrastructure assessment system. Accordingly, thecockpit 204 may be a mash-up application that combines information fromthese different data sources to re-present the data and offerspecialized services to the user. Several different instances of thecockpit module 204 may thus be customized for different business usersdepending on their area of interest. For instance, an energy providermay want a cockpit mash up to monitor energy consumption on the networkand get an alarm when energy consumption exceeds a limit or a device hasnot reported any meter readings for a specific amount of time. Amaintenance user may want a cockpit mash up to monitor the performanceof smart meters deployed in the smart grid infrastructure 102 and bealerted when performance levels (e.g., data reporting rate) falls belowa threshold.

As shown in FIG. 2A, the cockpit module 204 may an application (e.g., amash up) executing on a mobile device 262 such as a smart phone or acomputing tablet. Data sources in the infrastructure assessment system100 (e.g., modules 212, 222, 224, 232, 242, 244, and 256) may beconfigured with suitable web services interfaces, allowing the cockpitmodule 204 to access their data and otherwise interact with them fromthe mobile device. The infrastructure assessment system 100 may thusserve as a back-end system, allowing mobile users 104′ to access theinfrastructure assessment system from their mobile devices (e.g., 262)while they are in the field.

As shown in FIG. 2B, in some embodiments, the infrastructure assessmentsystem 100 may include a cockpit services module 204′, such as a webservices interface, to serve as a single point of access to the datasources in the infrastructure assessment system. The cockpit module 204in the mobile devices may interface with the cockpit services module204′. In some embodiments, the cockpit service module 204′ may beconfigured to communicate with the enterprise services layer 126, givingthe enterprise services layer access to the infrastructure assessmentsystem 100. The enterprise services layer 126 may be allowed toconfigure which aspects of the smart grid infrastructure 102 should bemonitored, the quality of the expected information, and so on. Inaddition, configurations may be customized on a per business processbasis. Although the cockpit service module 204′ may serve as aconvenient access of services, in other embodiments, the individualmodules of the infrastructure assessment system 100 may provide theirrespective services directly to the user 104′ instead.

Returning to FIG. 2, a key performance index (KPI) monitor 222 managesand monitors KPI's. KPI's represent operational metrics or parameters ofthe smart grid infrastructure 102 and thus may reflect how well thesmart grid infrastructure is behaving. The KPI's may be used toestablish a minimum level of requirements for the smart gridinfrastructure 102. Typical KPI's might include communication metrics,computation statistics, load limits and balancing, network pathutilization, congestion, application logic, etc. In the case of smartmetering, for instance, typical considerations may include:

-   -   impact of security: channel vs. message encryption, firewall        inspections, etc.    -   level of meter reading aggregation    -   preprocessing of meter readings at meter, concentrator or        network level    -   impact of channel communication quality e.g. latency, packet        loss, throughput, retransmission    -   metering data system performance (application processing, data        validation, DB performance etc.)    -   load management/balancing    -   cost (including lifecycle management of software and hardware)    -   risk analysis, resiliency    -   business process constraints integration    -   business process design-phase integration of asset management

A user (human user, enterprise services layer 126) may access servicesprovided by the KPI monitor 222 to define the KPI's of interest. Theuser may define how often KPI's get updated, and so on. The KPI monitor222 may receive incoming data collected by the receiver module 202 andupdate the KPI's using the collected data. In this way, the KPI monitor222 may provide a continuous monitoring of an aspect of the health ofthe smart grid infrastructure 102.

A historian 212 receives data collected by the receiver module 202, andaccumulates a historical record of the collected data. In someembodiments, the historian 212 may accumulate data collected from theenterprise services layer 122, the information processing layer 124, anddata from individual prosumer devices 132-136. The historian 212 maycomprise any suitable data storage and management system such as a highperformance database system. The historian 212 may be accessed by othercomponents of the infrastructure assessment system 100. The historian212 may provide data to external systems. For example, an auditingsystem (not shown) may access the historical data maintained by thehistorian 212 to conduct audits of the smart grid infrastructure 102.

The collected data may be subject to various analytics. The monitoringof KPI's by the KPI monitor 222 may include computations performed onthe incoming data received by the receiver module 202. An analyticsmodule 224 may provide a suite of analytical tools to allow a user todefine a broader range of analyses on the data collected by the receivermodule 202. The analytics module 224 may perform the defined analyticson the incoming data (e.g., real time analytics). In addition, theanalytics module may perform various analyses on the historical datamaintained by the historian 212 (e.g., trend analysis). Exampleanalytics may include technical analysis (e.g., statistics of deployedsmart meters or software etc.), real-time view and statistics on energyproduction/consumption/estimation, data mining, business relevantaspects (e.g., a cost-benefits analysis, etc.), risk, security and fraudanalytics, behavioral/social analytics, etc.

A simulator 232 may run simulations and emulations of the smart gridinfrastructure 102 and services provided by the smart gridinfrastructure such as monitoring energy consumption and production,forecasting energy consumption and production, managing users anddevices, optimizing distribution of energy, and so on. The simulator 232may comprise tools for developing simulation models for various aspectsof the smart grid infrastructure 102. Simulation models may includemodeling the physical configuration of the smart grid infrastructure102. For example, in an electrical grid, households, businesses, andother consumers of electricity may be modeled according to thedeployment of electrical distribution stations to model the delivery ofelectricity to end users in the smart grid infrastructure 102. Usagemodels may be developed to model electricity demands under differentconditions (e.g., time of year, disaster scenarios, etc.). Simulationmodels may include the enterprise services layer 126 and the informationprocessing layer 124, for example, to model the flow of data within thesmart grid infrastructure 102 when services are being performed. And soon.

A decision support module 242 facilitates identifying and developingaction items to be performed on the smart grid infrastructure 102. Insome embodiments, the decision support module 242 may provide tools toassist the user in conducting what-if scenarios on the smart gridinfrastructure 102. The user may employ data from the KPI monitor 222,data from the analytics module 224, and simulation results from thesimulator 232 to drive the what-if scenarios. In addition, historicaldata managed by the historian 212 may feed into the what-if scenarios.What-if scenarios may be used, for example, to assess new functionalityto be introduced into the network, to run “experiments’ on scalabilitystrategies for better overall performance, and so on.

In some embodiments, the decision support module 242 may operate in amonitoring mode. For example, the simulator 232 may feed some of itssimulation results into the decision support module 242. The simulator232 may generate predicted performance measures of the smart gridinfrastructure 102. The performance measures may be input to thedecision support module 242 to compare against actual behavior of thesmart grid infrastructure 102. Data from the KPI monitor 222 and datafrom the analytics module 224 may be used to establish the actualbehavior of the smart grid infrastructure 102, for example. Based oncomparing actual behavior versus simulated behavior, the decisionsupport module 242 may trigger certain action items in order to bringsimulations of the smart grid infrastructure and actual performance intoalignment. Action items may be performed on the smart gridinfrastructure 102 to align the performance of the smart gridinfrastructure in accordance with simulations. The action items mayinclude modifying simulation models to more closely match reality. Insome embodiments, the user may provide input to guide the decision asthe selection of action items.

An optimization strategies module 244 may include a library ofoptimization strategies to optimize certain behavior in the smart gridinfrastructure 102, or to achieve certain goals set by the user. Thedecision support module 242 may incorporate these optimizationstrategies to guide the decision process of identifying action items tobe taken. In some embodiments, the user may inform the decision supportmodule 242 by selecting a desired strategy. Different strategies bydifferent groups may apply to a given situation. Sometimes strategiesfrom one group (e.g., a business group) may conflict with strategiesfrom another group (e.g., a maintenance group). Accordingly, userinteraction may be required to resolve conflicts when the decisionsupport module 242 encounters conflicting strategies. As a result an“optimized” strategy may comprise action items that were negotiatedamong several groups in the organization or with interactions withexternal groups.

As an example, a local utility may run a strategy to optimize the energyconsumption by adjusting the tariffs in real-time (maximize benefit).However, this might be in conflict with an existing running process thattries to optimize the infrastructure for maintenance (hence minimizeusage and communication). Similar conflicts might arise if contradictingpolicies are given within the same organization or vastly differentgoals among different users (which may be not be in the same domain asseveral instances of this system may run). Here, it is assumed that suchpotential conflicts and negotiations are handled by the users, in theDSS itself, or with external help (e.g. negotiation with other systems).

In some embodiments, a management engine 252 may cooperate with thedecision support module 242 to manage the smart grid infrastructure 102in accordance with decisions made by the decision support module. Themanagement engine 252 may dispatch action items decided upon by thedecision support module. For example, the decision support module 242may identify an action item to conduct an installation of smart metersin a region in order to improve data gathering capacity in that region.The decision support module 242 may communicate the action item to themanagement engine 252, which may then issue a work order to amaintenance crew to initiate the installation effort. The managementengine 252 may be invoked directly by a user to perform some activity inthe smart grid infrastructure 102.

FIG. 3 illustrates a process flow in accordance with the principles ofthe present disclosure. In some embodiments, the infrastructureassessment system 100 can be utilized in the decision-making and designprocess prior to deployment of a smart grid in order to assess cost,performance, and behavior. Thus, in a step 302 a design group maydevelop simulation models to model the desired behavior of theto-be-deployed smart grid using the simulator 232. Desired KPI's may bedetermined (e.g., smart metering performance, scalability of the smartgrid, etc.) and incorporated into the simulation models. In this step,different near real world conditions and configurations can be tried outand assessed prior to any real world deployment. The functionalityexposed by the simulator 232 may be integrated into business processmodeling tools to extend simulations into the business side of the smartgrid.

In a step 304, the smart grid and infrastructure/systems may bedeployed. This step may occur in several phases over a period of manyyears. In a step 306, the infrastructure assessment system 100 may beused to monitor the smart grid and to detect deviations from expectedbehavior after the smart grid is built. The infrastructure assessmentsystem 100 may predict the behavior expected from the realinfrastructure using the simulator 232. The infrastructure assessmentsystem 100 may then subsequently measure the behavior and note anysignificant deviations between the simulation model and the real world.Such deviations may for instance imply unforeseen conditions, simulationmodel inadequacy or misbehavior at infrastructure level. A typicalexample might be identifying electricity loss or theft. Step 306 mayinclude monitoring the KPI's using the KPI monitor 222 and computingadditional analytics using the analytics module 224. The simulationmodels developed in step 302 may be run to make predictions aboutexpected KPI's and other performance metrics.

In a step 308, the decision support module 242 may use the collecteddata and the various computed data to decide upon action items to beperformed on the smart grid. In a step 310, the smart grid may bemanaged, for example, by one or more management engines 252 dispatchingaction items determined in step 308. As an example, suppose a meteringperformance KPI such as “data points collected” for a given region inthe smart grid has fallen below predetermined thresholds or if thequality of data coming from a device or system degrades, the decisionsupport system 242 may initiate a predictive maintenance analysis andidentify potential malfunctioning risks. A follow-up action item mightbe to create an action, such as “send out repair crew”. The action itemmay be communicated to a management engine 252, which may then generatea work order to send out a repair crew to inspect a number of the metersin that area (e.g., using an optimized repair schedule to reducedowntime, cost, and the like).

The steps 306-310 may be repeated to create a monitor/manage loop. Itwill be appreciated that this loop may be performed at each phase duringthe deployment of the smart grid. For example, the simulation models maybe updated as each phase of the deployment is monitored and its actualbehavior is measured and compared against the simulation models.Subsequent phases of the deployment may be altered to based on resultsof monitoring previous phases of the deployment. By supporting a closedloop of monitoring and control/management, the user can performself-examination activities such as self-healing and self-optimizing,both of which can be important to the health of the smart gridinfrastructure 102.

FIG. 4 illustrates another process flow in accordance with principles ofthe present disclosure. In some embodiments, the infrastructureassessment system 100 may be integrated into an existing smart grid.Thus, in a step 402 the existing smart grid may be modeled using toolsprovided by the simulator 232. KPI's may be defined using the KPImonitor 222, for example, to identify a baseline of performance metricsthat indicate how well the existing smart grid is behaving.

In a step 404, the infrastructure assessment system 100 may be operatedto monitor the existing smart grid. The KPI monitor 222 may update theKPI's as data is collected by the receiver component 202. The simulator232 may perform simulations on the models developed in step 402, and soon. In a step 406, the decision support module 242 may use the collecteddata and the various computed data (including simulation results) tomake decisions on action items to be performed on the existing smartgrid. In a step 408, the existing smart grid may be managed, forexample, by the management engine 252 dispatching the action itemdetermined in step 406. An output might also be given, for example, to arecommendation system or evaluation of the specific model or algorithmso that it can optimize its behaviour in the future. The steps 404-408may be repeated to create a monitor/manage loop.

A particular embodiment of the infrastructure assessment system 100 inaccordance with the present disclosure is illustrated in FIG. 5, showinga high level block diagram of a computer system 502 configured tooperate in accordance with the present disclosure. The computer system502 may include a central processing unit (CPU) or other similar dataprocessing component, which may comprise one or multiple processingunits, clusters of CPUs, etc. The computer system 502 may includevarious memory components. For example, the memory components mayinclude a volatile memory 514 (e.g., random access memory, RAM, virtualmemory system, etc.) and a data storage device 516. The data storagedevice 516 may be distributed storage system and not necessarilycollocated with the rest of the computer system 502. One or morecommunication interfaces 518 may be provided to allow the computersystem 502 to communicate over a wired or wireless communication network522, such as a local area network (LAN), the Internet, and so on. Ingeneral, any protocol over the specific communication network can beused. An internal system of busses for control and communication 520 mayinterconnect the components comprising the computer system 502.

The data storage device 516 may comprise a non-transitory computerreadable medium having stored thereon computer executable program code532. The computer executable program code 532 may be executed by the CPU512 to cause the CPU to perform steps of the present disclosure, forexample, as set forth in the description of FIG. 2. The data storagedevice 516 may store data 534 such as the KPI's, simulation results fromthe simulator 232, results of analytics produced by the analytics module224, and so on. In some embodiments, the storage device 516 may compriseseveral storage sub-systems. The historian 212 may accumulate historicaldata in a separate storage sub-system, for example.

A user (e.g., 104) may interact directly with the computer system 502using suitable user interface devices 542 such as the cockpit 204, orindirectly since the system's functionality may be part of complexfunctionality provided at higher levels in the system. They may include,for example, input devices such as a keyboard, a keypad, a mouse orother pointing device, and output devices such as a display. Alternativeinput and output devices are contemplated of course. The interfacedevice 542 may be a mobile device.

All systems and processes discussed herein may be embodied in programcode stored on one or more non-transitory computer-readable media. Suchmedia may include, for example, a floppy disk, a CD-ROM, a DVD-ROM, aFlash drive, magnetic tape, and solid state Random Access Memory (RAM)or Read Only Memory (ROM) storage units. It will be appreciated thatembodiments are not limited to any specific combination of hardware andsoftware. Elements described herein as communicating with one anotherare directly or indirectly capable of communicating over any number ofdifferent systems for transferring data, including but not limited toshared memory communication, a local area network, a wide area network,a telephone network, a cellular network, a fiber-optic network, asatellite network, an infrared network, a radio frequency network, andany other type of network that may be used to transmit informationbetween devices. Moreover, communication between systems may proceedover any one or more transmission protocols that are or become known,such as Asynchronous Transfer Mode (ATM), Internet Protocol (IP),Hypertext Transfer Protocol (HTTP) and Wireless Application Protocol(WAP).

ADVANTAGES AND TECHNICAL EFFECT

The infrastructure assessment system 100 according to principles of thepresent disclosure can achieve:

-   -   real-time infrastructure performance management: monitoring,        assessment and control    -   simulation and estimation of infrastructure behavior (including        services)    -   complexity management for end-users based on cross-layer (e.g.,        enterprise services layer 126, information processing layer 124,        and prosumer devices layer 122) data computation and real-time        analytics

The above description illustrates various embodiments of the presentdisclosure along with examples of how aspects of the present disclosuremay be implemented. The above examples and embodiments should not bedeemed to be the only embodiments, and are presented to illustrate theflexibility and advantages of the present disclosure as defined by thefollowing claims. Based on the above disclosure and the followingclaims, other arrangements, embodiments, implementations and equivalentswill be evident to those skilled in the art and may be employed withoutdeparting from the spirit and scope of the disclosure as defined by theclaims.

1. A computer implemented method for assessing an infrastructure for asmart grid comprising: collecting data management performance datarelating to performance of a data management infrastructure component ofthe smart grid; updating in real-time a plurality of key performanceindicators (KPI's) of the infrastructure using the data managementperformance data; performing real-time analytics on the data managementperformance data; performing simulations on models of the infrastructureand services provided by the infrastructure; determining a plurality ofactions to be performed on the infrastructure based on: differencesbetween actual behavior of the infrastructure and simulations of theinfrastructure; results of the real-time analytics; changes in theKPI's; and optimization strategies that are in effect; and dispatchingthe actions to be performed.
 2. The computer implemented method of claim1 wherein collecting the data includes receiving data from an enterpriseservice layer.
 3. The computer implemented method of claim 1 whereincollecting the data includes receiving data from an informationprocessing layer in the smart grid which collects data from a pluralityof prosumer devices in the smart grid and produces data that isforwarded on to an enterprise service layer.
 4. The computer implementedmethod of claim 1 wherein collecting the data includes receiving datadirectly from a plurality of prosumer devices in the smart grid.
 5. Thecomputer implemented method of claim 1 wherein the actual behavior ofthe infrastructure and the simulations of the infrastructure aredetermined for different layers of the infrastructure.
 6. The computerimplemented method of claim 1 wherein the optimization strategiescomprise action items negotiated among separate groups in anorganization or among different organizations.
 7. The computerimplemented method of claim 1 further comprising receiving input from auser that comprise the optimization strategies that are in effect. 8.The computer implemented method of claim 1 further comprising receivingconflict resolution input from a user when the step of determiningproduces actions which are in conflict with each other.
 9. A computersystem comprising: a data processor; a data storage system; and computerexecutable program code which, when executed by the data processor,causes the data processor to: collect data management performance datarelating to performance of a data management infrastructure component ofthe smart grid; update a plurality of key performance indicators (KPI's)of the infrastructure using the data management performance data;perform analytics on the data management performance data; performsimulations on models of the infrastructure and services provided by theinfrastructure; determine a plurality of actions to be performed on theinfrastructure based on: differences between actual behavior of theinfrastructure and simulations of the infrastructure; results of theanalytics; changes in the KPI's; and optimization strategies that are ineffect; and dispatch the actions to be performed.
 10. The computersystem of claim 9 wherein the data processor collects the data byreceiving data from an enterprise service layer.
 11. The computer systemof claim 9 wherein the data processor collects the data by receivingdata from an information processing layer in the smart grid whichcollects data from a plurality of prosumer devices in the smart grid andproduces data that is forwarded on to an enterprise service layer. 12.The computer system of claim 9 wherein the data processor collects thedata by receiving data directly from a plurality of prosumer devices inthe smart grid.
 13. The computer system of claim 9 wherein the actualbehavior of the infrastructure and the simulations of the infrastructureare determined for different layers of the infrastructure.
 14. Thecomputer system of claim 9 wherein the optimization strategies compriseaction items negotiated among separate groups in an organization. 15.The computer system of claim 9 wherein the computer executable programcode further causes the data processor to receive input from a user thatcomprise the optimization strategies that are in effect.
 16. Thecomputer system of claim 9 wherein the computer executable program codefurther causes the data processor to receive conflict resolution inputfrom a user when the step of determining produces actions which are inconflict with each other.
 17. A non-transitory computer readable storagemedium having stored thereon computer executable program code configuredto cause a computer system to perform steps of: collecting datamanagement performance data relating to performance of a data managementinfrastructure component of the smart grid; updating a plurality of keyperformance indicators (KPI's) of the infrastructure using the datamanagement performance data; performing analytics on the data managementperformance data; performing simulations on models of the infrastructureand services provided by the infrastructure; determining a plurality ofactions to be performed on the infrastructure based on: differencesbetween actual behavior of the infrastructure and simulations of theinfrastructure; results of the analytics; changes in the KPI's; andoptimization strategies that are in effect; and dispatching the actionsto be performed.
 18. The non-transitory computer readable storage mediumof claim 17 wherein collecting the data includes receiving data from anenterprise service layer.
 19. The non-transitory computer readablestorage medium of claim 17 wherein collecting the data includesreceiving data from an information processing layer in the smart gridwhich collects data from a plurality of prosumer devices in the smartgrid and produces data that is forwarded on to an enterprise servicelayer.
 20. The non-transitory computer readable storage medium of claim17 wherein collecting the data includes receiving data directly from aplurality of prosumer devices in the smart grid.